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  1. Article: Bioinformatics Approach to Identify Significant Biomarkers, Drug Targets Shared Between Parkinson's Disease and Bipolar Disorder: A Pilot Study.

    Hossain, Md Bipul / Islam, Md Kobirul / Adhikary, Apurba / Rahaman, Abidur / Islam, Md Zahidul

    Bioinformatics and biology insights

    2022  Volume 16, Page(s) 11779322221079232

    Abstract: Parkinson's disease (PD) is a neurodegenerative disorder responsible for shaking, rigidity, and trouble in walking and patients' coordination ability and physical stability deteriorate day by day. Bipolar disorder (BD) is a psychiatric disorder which is ... ...

    Abstract Parkinson's disease (PD) is a neurodegenerative disorder responsible for shaking, rigidity, and trouble in walking and patients' coordination ability and physical stability deteriorate day by day. Bipolar disorder (BD) is a psychiatric disorder which is the reason behind extreme shiftiness in mood, and frequent mood inversion may reach too high called mania. People with BD have a greater chance of developing PD during the follow-up period. A lot of work has been done to understand the key factors for developing these 2 diseases. But the molecular functionalities that trigger the development of PD in people with BD are not clear yet. In our study, we are intended to identify the molecular biomarkers and pathways shared between BD and PD. We have investigated the RNA-Seq gene expression data sets of PD and BD. A total of 45 common unique genes (32 up-regulated and 13 down-regulated) abnormally expressed in both PD and BD were identified by applying statistical methods on the GEO data sets. Gene ontology (GO) and BioCarta, KEGG, and Reactome pathways analysis of these 45 common dysregulated genes identified numerous altered molecular pathways such as mineral absorption, Epstein-Barr virus infection, HTLV-I infection, antigen processing, and presentation. Analysis of protein-protein interactions revealed 9 significant hub-proteins, namely RPL21, RPL34, CKS2, B2M, TNFRSF10A, DTX2, HLA-B, ATP2A3, and TAPBP. Significant transcription factors (IRF8, SPI1, RUNX1, and FOXA1) and posttranscriptional regulator microRNAs (hsa-miR-491-3p and hsa-miR-1246) are also found by analyzing gene-transcription factors and gene-miRNAs interactions, respectively. Protein-drug interaction analysis revealed hub-protein B2M's interaction with molecular drug candidates like
    Language English
    Publishing date 2022-02-23
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2423808-9
    ISSN 1177-9322
    ISSN 1177-9322
    DOI 10.1177/11779322221079232
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Book ; Online: Generative AI-driven Semantic Communication Framework for NextG Wireless Network

    Raha, Avi Deb / Munir, Md. Shirajum / Adhikary, Apurba / Qiao, Yu / Hong, Choong Seon

    2023  

    Abstract: This work designs a novel semantic communication (SemCom) framework for the next-generation wireless network to tackle the challenges of unnecessary transmission of vast amounts that cause high bandwidth consumption, more latency, and experience with bad ...

    Abstract This work designs a novel semantic communication (SemCom) framework for the next-generation wireless network to tackle the challenges of unnecessary transmission of vast amounts that cause high bandwidth consumption, more latency, and experience with bad quality of services (QoS). In particular, these challenges hinder applications like intelligent transportation systems (ITS), metaverse, mixed reality, and the Internet of Everything, where real-time and efficient data transmission is paramount. Therefore, to reduce communication overhead and maintain the QoS of emerging applications such as metaverse, ITS, and digital twin creation, this work proposes a novel semantic communication framework. First, an intelligent semantic transmitter is designed to capture the meaningful information (e.g., the rode-side image in ITS) by designing a domain-specific Mobile Segment Anything Model (MSAM)-based mechanism to reduce the potential communication traffic while QoS remains intact. Second, the concept of generative AI is introduced for building the SemCom to reconstruct and denoise the received semantic data frame at the receiver end. In particular, the Generative Adversarial Network (GAN) mechanism is designed to maintain a superior quality reconstruction under different signal-to-noise (SNR) channel conditions. Finally, we have tested and evaluated the proposed semantic communication (SemCom) framework with the real-world 6G scenario of ITS; in particular, the base station equipped with an RGB camera and a mmWave phased array. Experimental results demonstrate the efficacy of the proposed SemCom framework by achieving high-quality reconstruction across various SNR channel conditions, resulting in 93.45% data reduction in communication.
    Keywords Computer Science - Networking and Internet Architecture
    Subject code 004
    Publishing date 2023-10-13
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: Performance evaluation of micro lens arrays: Improvement of light intensity and efficiency of white organic light emitting diodes.

    Adhikary, Apurba / Bhuiya, Joy / Murad, Saydul Akbar / Hossain, Md Bipul / Uddin, K M Aslam / Faysal, Md Estihad / Rahaman, Abidur / Bairagi, Anupam Kumar

    PloS one

    2022  Volume 17, Issue 5, Page(s) e0269134

    Abstract: This paper proposes a unique method to improve light intensity and efficiency of white organic light emitting diodes (OLEDs) by engraving micro lens arrays (MLAs) on the outer face of the substrate layer. The addition of MLAs on the substrate layer ... ...

    Abstract This paper proposes a unique method to improve light intensity and efficiency of white organic light emitting diodes (OLEDs) by engraving micro lens arrays (MLAs) on the outer face of the substrate layer. The addition of MLAs on the substrate layer improves the light intensity and external quantum efficiency (EQE) of the OLEDs. The basic OLED model achieved an EQE of 14.45% for the effective refractive index (ERI) of 1.86. The spherical and elliptical (planoconvex and planoconcave) MLAs were incorporated on the outer face of the substrate layer to increase the EQE of the OLEDs. The maximum EQE of 17.30% was obtained for Convex-1 (elliptical planoconvex) MLA engraved OLED where the ERI was 1.70. In addition, Convex-1 MLA engraved OLED showed an improvement of 3.8 times on the peak electroluminescence (EL) light intensity compared to basic OLED. Therefore, Convex-1 MLA incorporated OLED can be considered as a potential white OLED because of its excellent light distribution and intensity profile.
    MeSH term(s) Lenses ; Light ; Refractometry
    Language English
    Publishing date 2022-05-27
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2267670-3
    ISSN 1932-6203 ; 1932-6203
    ISSN (online) 1932-6203
    ISSN 1932-6203
    DOI 10.1371/journal.pone.0269134
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Book ; Online: MP-FedCL

    Qiao, Yu / Munir, Md. Shirajum / Adhikary, Apurba / Le, Huy Q. / Raha, Avi Deb / Zhang, Chaoning / Hong, Choong Seon

    Multiprototype Federated Contrastive Learning for Edge Intelligence

    2023  

    Abstract: Federated learning-assisted edge intelligence enables privacy protection in modern intelligent services. However, not independent and identically distributed (non-IID) distribution among edge clients can impair the local model performance. The existing ... ...

    Abstract Federated learning-assisted edge intelligence enables privacy protection in modern intelligent services. However, not independent and identically distributed (non-IID) distribution among edge clients can impair the local model performance. The existing single prototype-based strategy represents a class by using the mean of the feature space. However, feature spaces are usually not clustered, and a single prototype may not represent a class well. Motivated by this, this paper proposes a multi-prototype federated contrastive learning approach (MP-FedCL) which demonstrates the effectiveness of using a multi-prototype strategy over a single-prototype under non-IID settings, including both label and feature skewness. Specifically, a multi-prototype computation strategy based on \textit{k-means} is first proposed to capture different embedding representations for each class space, using multiple prototypes ($k$ centroids) to represent a class in the embedding space. In each global round, the computed multiple prototypes and their respective model parameters are sent to the edge server for aggregation into a global prototype pool, which is then sent back to all clients to guide their local training. Finally, local training for each client minimizes their own supervised learning tasks and learns from shared prototypes in the global prototype pool through supervised contrastive learning, which encourages them to learn knowledge related to their own class from others and reduces the absorption of unrelated knowledge in each global iteration. Experimental results on MNIST, Digit-5, Office-10, and DomainNet show that our method outperforms multiple baselines, with an average test accuracy improvement of about 4.6\% and 10.4\% under feature and label non-IID distributions, respectively.

    Comment: Accepted by IEEE Internet of Things
    Keywords Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Distributed ; Parallel ; and Cluster Computing
    Subject code 006
    Publishing date 2023-04-01
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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